2012
DOI: 10.3837/tiis.2012.10.012
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A Fair-Exchange E-Payment Protocol For Digital Products With Customer Unlinkability

Abstract: Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method … Show more

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Cited by 5 publications
(3 citation statements)
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References 26 publications
(34 reference statements)
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“…The primitive e-payment scheme was proposed by Chaum (2013). After this scheme many e-payment schemes were proposed (Chang et al, 2005;Chen et al, 2014;Xiaojun, 2010;Yen et al, 2012;Zhang et al, 2011). We will show that our e-payment scheme is more efficient than the existing epayment schemes.…”
Section: Introductionmentioning
confidence: 80%
“…The primitive e-payment scheme was proposed by Chaum (2013). After this scheme many e-payment schemes were proposed (Chang et al, 2005;Chen et al, 2014;Xiaojun, 2010;Yen et al, 2012;Zhang et al, 2011). We will show that our e-payment scheme is more efficient than the existing epayment schemes.…”
Section: Introductionmentioning
confidence: 80%
“…The primitive e-payment system was proposed by Chaum [4], after then many e-payment systems are proposed [3,[5][6][7][8][9][10]. While E-commerce is on its way to make daily life more convenient and easy, the main concerns in any e-payment system are security and privacy of participant and contents.…”
Section: Introductionmentioning
confidence: 99%
“…Previously methods build image annotation model based on three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Assuming targets are independent to each other, they managed to establish the relations between images and labels [1][2][3][4][5][6][7][8]. Despite its powerful discriminative ability, it cannot detect those visually hard-to-detect targets, such as small and blured objects.…”
Section: Introductionmentioning
confidence: 99%